Healthy water resources are still one of the main challenges in most countries, particularly less developed regions in dry parts of the world (WHO/UNICEF, 2014; Andrade et al., 2018). The lack of sufficient and appropriate water resources increases the use of contaminated water sources and wastewater for drinking, washing, and irrigation. Accordingly, employing contaminated water not only directly increases the risk of contamination by waterborne pathogens but also enhances the risk of contamination of vegetables with waterborne pathogens, particularly in farmlands irrigated using wastewater (Karanis et al., 2006; Javanmard et al., 2019, 2020).

Waterborne diseases include a large group of illnesses with mild to severe symptoms, which are caused by a broad spectrum of pathogens including bacteria, fungi, parasites, and viruses (Kalyoussef and Feja, 2014). Parasites, both protozoa and helminths, are important group of foodborne and waterborne pathogens, which can be transmitted from wild and domestic animals and contaminated water resources to humans (Sharma and Mutharasan, 2013; Vasilescu and Marty, 2016; Pazoki et al., 2020). The transmission route of foodborne and waterborne protozoa (FWP) is mostly oral-fecal and infection occurs when cysts or oocysts of a parasite are unintentionally ingested by a host (Kalyoussef and Feja, 2014).

Waterborne protozoa are responsible for a considerable number of outbreaks in the world. Regarding a recently published systematic review, 86.7% of eastern African countries reported at least one waterborne parasite from 1953 to 2019 (Ngowi, 2020). However, there is no periodical monitoring strategy for the detection of waterborne parasites (particularly protozoa) in less-developed regions like African countries. In contrast, outbreak surveillance in Europe between 2000 and 2007 indicated the presence of 354 and 70 outbreaks, related to drinking water and bathing water, respectively, in which protozoa were responsible for 17 (4.7%) drinking water and 38 (54.3%) bathing water outbreaks (ENHIS, 2009). Moreover, it was estimated that ~7,150,000 (95% CrI 3,880,000–12,000,000; 21.3%) illnesses in the United States in 2014 were associated with waterborne agents, of which Giardia lamblia and Cryptosporidium spp., were responsible for 415,000 (95% CrI 140,000–816,000) and 322,000 (95% CrI 61,700–993,000) illnesses, respectively (Collier et al., 2021). In addition, more than 10% of the total waterborne outbreaks from 1991 to 2008 were attributed to parasitic agents, particularly protozoa (Karanis et al., 2006; Efstratiou et al., 2017). More recently, Cryptosporidium spp., G. lamblia, Cyclospora cayetanensis, Toxoplasma gondii, Blastocystis sp., Entamoeba histolytica, microsporidia, and Naegleria fowleri were reported as the main causative agents detected in 251 waterborne outbreaks from 2017 to 2020 (Ma et al., 2022). Interestingly, most waterborne outbreaks due to protozoa have been reported in developed countries, indicating the importance of periodical monitoring of water resources, as well as diagnostic capabilities (Ma et al., 2022).

Significant progress has been made in recent decades in developing portable, reusable, and effective miniaturized systems or point-of-care (POC) platforms. POC tests can be performed outside a clinical laboratory setting, at or near the site of patient care. In addition, along with climate change, particularly in recent years, the risk of transmission of infectious diseases and the number of areas, which were previously unaffected by specific infectious diseases, have increased (Mora et al., 2022). In contrast, global warming, as a coming challenge in the world, decreases access to healthy water resources and increases emerging water and food safety concerns (Duchenne-Moutien and Neetoo, 2021). In fact, drought due to climate changes increases seasonal water resources, which aggregates animals and human communities in a region, recycled water resources, groundwater, and even lagoon water, and therefore, the risk of transmission of potentially pathogenic microorganisms, particularly parasites, from animals to humans (Titcomb et al., 2021). Moreover, because of the presence of a resistant stage, cyst/oocyst/egg, in the life cycle of parasites, particularly protozoa, these microorganisms endure harsh conditions such as drought much more than other microorganisms. All of these reasons highlight the importance of protozoa infections and the development of POC techniques for the detection of these parasites in the future. Guidelines, commonly needed for establishing well-organized POC systems, are presented by the World Health Organization (WHO). These guidelines are identified as ASSURED, in which the abbreviation ASSURED stands for affordable, sensitive, specific, user-friendly, rapid, equipment-free, or minimal, and delivered to those who require them (Syedmoradi et al., 2017, 2021; Omidfar et al., 2020).

This study highlights the need for rapid, selective, and easy-to-use technology for the early detection of common waterborne parasitic pathogens. The purpose of the current review is to provide a comprehensive overview of conventional methods and emerging biosensors and nanobiosensors, with a focus on recent advances in smart-based devices.

Although a broad spectrum of parasites is reported from waterborne outbreaks, Cryptosporidium spp., G. lamblia, T. gondii, and E. histolytica are among the most frequently detected waterborne parasites in the world (Al-Shamiri et al., 2010; Robert-Gangneux and Dardé, 2012; Plutzer and Karanis, 2016; Sarkari et al., 2016; Ma et al., 2022). Nevertheless, microsporidia, Blastocystis sp., Dientamoeba fragilis, Balantidium coli, C. cayetanensis, and Isospora belli are the neglected waterborne parasites (Karanis et al., 2006; Plutzer and Karanis, 2016). Regarding the worldwide waterborne outbreaks reported by Baldursson and Karanis (2011), from 2004 to 2010, Cryptosporidium spp., and G. lamblia were the major causative agents in 60.3% (120) and 35.2% (70) of 199 outbreaks, respectively, while T. gondii, C. cayetanensis, Acanthamoeba spp., E. histolytica, and Blastocystis sp., were the other reported agents. Recently, an update on waterborne outbreaks due to parasites from 2017 to 2020 signified the high prevalence of Cryptosporidium spp., and G. lamblia as the major reported agents, followed by D. fragilis, T. gondii, C. cayetanensis, Blastocystis sp., E. histolytica, N. fowleri, and microsporidia (Ma et al., 2022).

Diagnosis plays a critical role in the discovery of new pathogens, monitoring and surveillance, the prediction of epidemics and pandemics due to emerging and re-emerging pathogens, and antibiotic resistance (Mohammad Rahimi et al., 2019). In recent decades, various methods have been developed for the diagnosis of intestinal parasites. However, conventional diagnostic methods are still employed for the detection of intestinal parasites, particularly in less-developed regions (Mohammad Rahimi et al., 2019). Accordingly, the detection of G. lamblia, Cryptosporidium spp. and E. histolytica is mainly based on the optical detection of cysts/oocysts and trophozoites of parasites using microscopy (Destura et al., 2015; Ricciardi and Ndao, 2015; Hooshyar et al., 2019), while the most common diagnostic method for T. gondii is immunoassay techniques (Elgun and Koltas, 2011; Rostami et al., 2018).

Despite the advantages of microscopic methods, there are some limitations such as the technique being time-consuming and optical skills of laboratory technicians (Laude et al., 2016; Sakamoto et al., 2018). In addition, due to the low number of FWP in a large volume of environmental samples like water, employing microscopic techniques is an important challenge. For example, the concentration of G. lamblia in water samples has been reported to be 0.01 to 100 cysts/L (WHO, 2014); thus, developing methods for the detection of 1 cyst/oocyst of G. lamblia and Cryptosporidium spp., in 10–100 L of water samples is desirable, particularly when we consider that Cryptosporidium spp., and G. lamblia are among the waterborne pathogens with a high priority and with infectivity dosage less than 10 oocysts/cysts (WHO, 2014). In contrast, the development of molecular techniques in recent decades has overcome the limitations of conventional methods and has provided more sensitivity and specificity for the detection of pathogens (Tavares et al., 2011).

Although progress has been made in molecular techniques, there are still some disadvantages and limitations that restrict the application of molecular techniques. Multiple steps including DNA or RNA extraction, primer design challenges, false results due to undesirable primer interactions, and expensive equipment are the common challenges facing molecular methods (Garibyan and Avashia, 2013; Khurana and Chaudhary, 2018). Regarding the abovementioned limitations of conventional and advanced molecular methods, designing high-efficiency and field-adopted diagnostic devices with a simple user interface and a rapid protocol is the main priority (Luka et al., 2019) (Table 1).

In recent years, advanced devices have been developed to overcome the limitations of available techniques. For example, the sensitivity of microscopic techniques is not high enough, and a well-trained technician is needed to reduce the possibility of a false report. In addition, the staining procedure may take much time; these reasons are challenges at the time of outbreaks. In contrast to microscopic methods, serological and molecular techniques are not labor intensive, provide high sensitivity and specificity, and do not need a microscopist. Nevertheless, serological methods may provide cross reaction, and are not suitable for screening a population at the time of outbreaks or most intestinal FWP. In addition, due to expensive equipment and instruments for molecular methods, and the need for a well-equipped laboratory, most molecular-based approaches are not suitable for investigation of an outbreak (Mohammad Rahimi et al., 2019; Mahdavi Abhari et al., 2023). Therefore, focus has been dramatically increased on biosensors, such as POC tests, which can provide enough sensitivity and specificity, without the need for a well-trained technician or a specific facility.

The importance of food and water safety in various industries has led to the mining and improvement of nanoscale analytical devices known as nanobiosensors. Due to the numerous advantages of these devices such as portability, low cost, rapid assay time, ease of use, and high selectivity and sensitivity (Terry et al., 2005; Ahmadi et al., 2022; Khoshfetrat et al., 2022; Saeidi et al., 2022), particularly for the detection of infectious agents and pollutants in the environment (Pejcic et al., 2006; Sin et al., 2014; Ahmadi et al., 2021c), focus on the fabrication of biosensors as a diagnostic technology for the detection of different analytes in food, water, and environmental samples has intensely increased (Terry et al., 2005; Pejcic et al., 2006; Metkar and Girigoswami, 2018; Salouti and Khadivi derakhshan, 2020) (Figure 1).

A biosensor consists of at least two functional elements, namely, a molecular recognition element (receptor), which selectively interacts with its target analyte, and a physicochemical transducer. Biological elements are generally classified into enzymes, antibodies, and nucleic acids. A transducer is a component of biosensors which plays an important role in the signal detection process, and converts biological responses into a measurable signal with high quality (Khodaei et al., 2019; Ahmadi et al., 2021a,b).

Biosensors are generally classified into label-free and labeled. The latter biosensor employs labeled molecules for the detection of a target (Proll et al., 2007; Rhouati et al., 2016). Common labeling platforms are fluorescence or luminescence labeling, radiolabeling, isotope labeling, and enzymes (Deline and Nason, 2019; Ranjbar Bahadori et al., 2021). In these procedures, the final sensor signal represents the number of labels bound to target molecules. As a drawback, label-based technologies are labor- and cost-intensive and time-consuming (Cunningham and Laing, 2008). In addition, labeling of biomolecules can block active binding sites and alter the binding properties (Schöning and Poghossian, 2018).

In contrast, label-free biosensing technologies do not employ labels to facilitate measurements and instead incorporate the intrinsic physical properties of an analyte, such as the molecular weight, size, charge, electrical impedance, dielectric permittivity, and refractive index of a sample. In recent years, label-free biosensors have been developed due to their ability for rapid and inexpensive bio-detection in small reaction volumes (Schöning and Poghossian, 2018). Moreover, they can be integrated into lab-on-a-chip platforms, allowing monitoring of target analytes in real time. Label-free biosensors are usually designed based on optical, electrical or electrochemical, and acoustic parameters (Citartan et al., 2013).

Based on the biological elements, biosensors are categorized into genosensors, immunosensors, and aptasensors (Low et al., 2012; Kokkinos et al., 2016; Campuzano et al., 2017; Mohammed et al., 2017; Felix and Angnes, 2018).

In genosensors, oligonucleotide sequences (DNA or RNA) are usually employed as bio-receptors, which are immobilized onto the transducer surface and hybridized with the single-stranded target DNA. In fact, the oligonucleotide sequences, such as a probe, recognize the analyte (sample DNA or RNA) by matching with the complementarity sequences. Genosensor-based devices are widely used for the detection of a broad spectrum of pathogens (Drummond et al., 2003; Babkina and Budnikov, 2006; Gao et al., 2010; Mohammed et al., 2017) (Figure 2A).

Immunosensors play an important role in the evaluation of specific elements in biological fluids. Currently, such assays have been extensively utilized in food safety and environmental analysis (Felix and Angnes, 2018; Hosu et al., 2018) (Figure 2B).

A single-stranded functional nucleic acid or peptide with a strong receptor property is recognized as an aptamer. Aptamers are usually constructed from combinatorial single-stranded libraries by the systematic evolution of ligands using the exponential enrichment (SELEX) method, and are applied to detect multiple target analytes (Shamah et al., 2008; Liu et al., 2020). Aptamer-based techniques have been applied for the detection of numerous pathogens such as human immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV), Mycobacterium, Salmonella, Listeria, Staphylococcus, Clostridium, Bacillus, Escherichia, Aspergillus, Penicillium, SARS-CoV, influenza virus, respiratory syncytial virus (RSV), Trypanosome, Plasmodium, Cryptosporidium, and Leishmania (Cho et al., 2011; Nagarkatti et al., 2012; Martín et al., 2013; Iqbal et al., 2015; Babamiri et al., 2018; Lavania et al., 2018; Li et al., 2018, 2020; Suh et al., 2018; Wei et al., 2018; Xi et al., 2018; Cai et al., 2019; Singh et al., 2019; Zou et al., 2019). The number of aptasensor-based studies for the detection of FWP is low. In this regard, Iqbal et al. (2015) developed an electrochemical nanomaterial-based aptasensor using a gold nanoparticle (NP)-modified screen-printed carbon electrode (SPCE) to detect C. parvum oocysts in spiked fresh fruits. In this system, 14 aptamer colons were discovered and anti-aptamer and thiolated DNA primers were mixed to produce a hybrid compound that was assembled onto the SPCE. The fabricated aptasensor recognized C. parvum with a wide dynamic range from 150 to 800 oocysts and a detection limit of ~100 oocysts. This study suggested promising findings for the detection of C. parvum in food products (Iqbal et al., 2015) (Table 2; Figure 3).

Improvements in nanotechnology science have provided the opportunity for researchers to work at nanoscale levels. Nanomaterials range from 1 to 100 nm and are classified into various groups, namely, nanoparticles (NPs), quantum dots (QDs), carbon nanotubes (CNTs), graphene, graphene oxide (GO), and nanochannels (Avant et al., 2019; Saleem and Zaidi, 2020; Pandey and Chusuei, 2021). Incorporating NPs with a biosensor system is performed for modifying and improving the sensor platforms and to overcome the limitations of conventional diagnosis tools (Luo et al., 2006). NPs are employed in the main types of biosensor systems including electrochemical, calorimetric, optical, and acoustic (Malik et al., 2013). NPs have critical functions such as reducing the time of reactions, catalysis, and immobilization of biomolecules (nucleic acid, antibody, and enzymes) into the electrochemical biosensors (Luo et al., 2006). In addition, due to their large specific surface, NPs are useful for improving electron transfer between biomolecules and the surface electrode (Cho et al., 2020). Common NPs include metal NPs (Au, Ag, and Pt), oxide NPs (SiO₂, TiO₂, ZrO₂, and MnO₂), and semiconductor NPs (CdS and PbS); metal NPs are more common due to their excellent catalytic properties in catalyzing electrochemical reactions (Tang et al., 2005). Moreover, silver and gold NPs have excellent conductivity properties, which enhance electron transfer between biomolecules and electrodes (Alaqad and Saleh, 2016). Moreover, gold NPs play an important role in increasing the sensitivity of electrochemical biosensors (Saha et al., 2012).

Magnetic NPs (MNPs) are used for designing magnetic biosensors, which have been broadly applied in medical areas such as diagnostic assays, DNA or RNA isolation, magnetic resonance imaging (MRI), and drug delivery (Khoo et al., 1997; Kudr et al., 2017; Wang et al., 2017; Ali et al., 2021). MNPs are a class of nanomaterials composed of metals such as cobalt, nickel, and iron, with paramagnetic, ferromagnetic, and superparamagnetic properties (Aboul-Enein et al., 1999; Akbarzadeh et al., 2012). Magnetic iron oxide NPs are employed for biomedical applications such as magnetic separation (Wu et al., 2015). For example, immunomagnetic separation (IMS) is now employed as a standard method for the detection and separation of Cryptosporidium spp., and G. lamblia oocysts/cysts from 10 to 150 L water samples (USEPA, 2012), although several studies have been conducted to increase the recovery rate of the method using either additional concentration methods or alternative elution (Hu et al., 2004; Fradette and Charette, 2022).

Oxide NPs have several chemical properties and possess a high surface energy (Stankic et al., 2016). For example, MnO₂ NPs can directly react with biomolecules (Vukojević et al., 2018). In addition, oxide NPs, for instance, SiO₂ NPs, can also be used as labels for biomolecules. SiO₂ NPs, as an oligonucleotide label, have been used for electrochemical sensitive detection in genosensors and immunosensors (Ma et al., 2008; Wang et al., 2013).

Quantum dots are semiconductor nanocrystals made up of a reactive core, which contains semiconductor particles such as cadmium selenide (CdSe), cadmium telluride (CdTe), indium phosphide (InP), or zinc selenide (ZnSe). However, QDs, as ideal materials, have been widely used in the development of sensing technology due to their extraordinary chemical properties such as excellent optical aspects (Ding et al., 2022).

Carbon nanotubes are the most popular advanced sensing technology, and have recently attracted interest for their unique properties such as excellent electronic conductivity features and large surface-to-volume ratios (Zaporotskova et al., 2016). Nanotubes have cylindrical structures with several hexagonal graphite planes rolled in tubes, which are divided into single-walled NTs (SWNTs) and multi-walled NTs (MWNTs), based on the number of walls (Saxena and Srivastava, 2020).

Graphene and GO nanomaterials present unique chemical and electrical features, which have highlighted them as promising materials to improve signal responses in novel sensing technologies such as electrochemical biosensors, fluorescence resonance energy biosensors transfer (FRET), laser desorption/ionization mass spectrometry (LDI-MS), and surface-enhanced Raman spectroscopy (SERS) (Chauhan et al., 2017; Janegitz et al., 2017; Morales-Narváez et al., 2017). GO possesses a hydrophobic domain structure and hydrophilic oxygen-containing functional groups, which provide good biocompatibility and water dispersibility (Ghulam et al., 2022). However, their features, including high surface area and a high affinity for a variety of biomolecules (antibodies, enzymes, DNA, cells, and proteins), have made them ideal for next-generation biosensors (Lee et al., 2016).

The recent trends are the use of both single and array nanochannels for electrical biosensing applications. Graphene and its analogs are among the emerging materials used to obtain nanochannels (de la Escosura-Muñiz and Merkoçi, 2012). The applications of nanochannels are focused on the detection of DNA, protein, virus, toxin, and other analytes (Wang et al., 2015; Sun et al., 2016; Shiohara et al., 2022).

The biosensor system employs a sensing technique and reacts with an analyte to produce a measurable electrochemical, electrical, mechanical, optical, or thermal signal (Mehrotra, 2016; Naresh and Lee, 2021). Biosensors can also be classified as electrochemical, optical or mechanical biosensors (Cammann, 1977; Thevenot et al., 1999; Ronkainen et al., 2010; Bermejo et al., 2011; Monosik et al., 2012; Ozdemir et al., 2013).

Microfluidic systems are geometrically small scale (typically sub-millimeter) and can be incorporated with biosensor systems. Theoretically, microfluidic devices are comprised of thin grooves or small wells, channels, micro-channels, and chambers. These devices are rapid and accurate, and are increasingly employed for the detection of waterborne pathogens (Woolley and Mathies, 1994; Stone et al., 2004; Fiorini and Chiu, 2005; Chin et al., 2012).

In a biosensor-independent manner, there is a commercial microfluidic device for the detection of Cryptosporidium oocysts in water samples with on-chip integrated sample preparation features, named CryptoDetect CARD. This technology involves integrated immunomagnetic separation (IMS); however, the technology needs more development to specify the LoD and sensitivity, and the need for sample preparation, filtration, and concentration still limits its use (Rheonix, 2011).

As a first strategy, hydrodynamic trapping together with immunofluorescence detection was utilized (Zhu et al., 2004; Taguchi et al., 2005, 2007; Lay et al., 2008; Mudanyali et al., 2010). In this regard, Taguchi et al. (2005) developed a micro-well array strategy to capture oocysts. For trapping oocysts in wells, micro-wells with a 10 μm or 30 μm diameter and a 10 μm depth were developed to capture oocysts of Cryptosporidium. This technology was able to detect the oocysts in very small sample volumes and could therefore be used instead of visual inspection of microscope slides. The micro-wells were coated with streptavidin and anti-C. parvum antibodies, and the samples containing C. parvum oocysts (10⁷ oocysts/mL) suspended in PBS were simply deposited onto the array. This approach was able to detect Cryptosporidium oocysts for 60 min at a maximum flow rate of 350 μL/min (5 mL could be processed in under 15 min), with a LoD of 36 oocysts/mL (Taguchi et al., 2005). Diéguez et al. (2018) purposed a disposable microfluidic micromixer, which was able to specifically capture, isolate, and concentrate Cryptosporidium from water samples. This designed device was able to analyze the quantification of captured oocysts using immunofluorescence microscopy, as well as an imaging flow cytometer. In addition, the microfluidic micromixer device provided a rapid and efficient detection method, with a capture efficiency of 96% compared to other available laboratory-scale technologies for the detection of Cryptosporidium oocysts (Figure 8A).

In addition, a microfluidic inertial separation chip was designed and fabricated for the separation of Giardia cysts from food samples (Ganz et al., 2015). The microfluidic chips consisted of an inlet, a main separation channel with a rectangular microfluidic channel, and a large channel, which was divided into three smaller channels connected to three output channels. The method was very efficient and specific for G. lamblia, and recovered an average of 68.4% of cysts, with a LoD of 38 cysts from a 25 g lettuce sample (Ganz et al., 2015) (Figure 8B).

Hydrodynamic trapping of Cryptosporidium oocysts, either in wells or filters, through pre-filter structures or a raindrop filter, was also developed. This microfluidic device was incorporated into a SUS micromesh to capture C. parvum oocysts. Trapped C. parvum oocysts were visualized by fluorescent staining. The concentration of added C. parvum oocysts and oocysts detected by the SUS micromesh was linearly correlated within the range of 18–200 oocysts/mL. The results of this technique were in agreement with the direct immunofluorescence assay coupled with the immunomagnetic separation (DFA-IMS) method, while the recovery of SUS micromesh (93%) was higher than DFA-IMS (90%), suggesting that the SUS micromesh is a promising procedure for counting C. parvum oocysts (Taguchi et al., 2007) (Figure 9).

As a second strategy, it was illustrated that trapping of Cryptosporidium oocysts using sieves or filters may increase the sensitivity of microfluidic systems (Zhu et al., 2004; Lay et al., 2008). A fully automated system consisting of a filtration unit and pumping system (1,000 L within 24 h), complemented by a microfluidic chip, Crypto-Tect bio-slide, was developed by the Shaw Water Ltd. Company, which stained and counted Cryptosporidium oocysts with a LoD slightly higher than 10 oocysts (Shaw, 2009), while 1,000 L drinking water was concentrated to a 1.5 mL sample, which was suitable for introducing to the microfluidic system. In addition, a microfluidic device based on the detection of C. parvum and G. lamblia oocysts/cysts using positive pressure was developed that identified C. parvum and G. lamblia fluorescent labels, while the staining solution was 10 to 100 times more diluted than the recommended concentration in the conventional glass method (Zhu et al., 2004).

Microfluidic trapping devices can also be integrated with on-chip molecular methods for further applications (Mahdavi Abhari et al., 2023). Molecular sensing techniques include pre-amplification of the microorganism genomic material, either via fluorescence or electrochemical tools. These reliable and rapid detection techniques still require genomic materials. The detection of C. parvum in water resources still requires the parasite to be collected and concentrated from a large water sample volume. In fact, during the analysis of water samples, the numbers of recovered parasites are usually low and cannot be detected without DNA amplification. To overcome this limitation, Esch et al. (2001), developed a microfluidic chip, which was amplified by nucleic-acid-sequence-based amplification (NASBA), using DNA-modified liposomes to detect RNA in viable C. parvum. A NASBA-generated amplicon was placed between the capture and reporter probes in a microfluidic channel. To generate fluorescence, reporter probes were tagged with carboxyfluorescein-filled liposomes, which increased the sensitivity of detection, even in very low concentrations of targets. The LoD of the microfluidic chip was reported to be 5 fmol of amplicon in 12.5 μL of sample solution (Figure 10).

Recent developments have been presented in novel engineering systems for the detection of Cryptosporidium and G. lamblia based on the integration of electrochemical biosensors into microfluidic systems. A microfluidic impedance cytometry (MIC) system based on the detection of viable parasites was proposed and designed by McGrath et al. (2017), which was able to rapidly discriminate live and inactive C. parvum oocysts with over 90% certainty, and to identify the viability of Cryptosporidium and Giardia at the single (oo)cyst level (Figure 11; Table 3).

In recent years, smartphone microscopic methods have been described and used as an alternative platform for the detection of targeted pathogens, incorporated with traditional optical microscopic methods. The strengths of these techniques are the low cost, small size, being portable, and easy transportation to rural and remote settings. In fact, portable devices that can transmit relevant data to remote experts have a large impact on the quantity and quality of care. In addition, cell phone cameras are the most ubiquitous optical sensors in the world (Breslauer et al., 2009; Rajchgot et al., 2017).

In the field of waterborne protozoa, Shrestha et al. (2020) developed a smartphone-based microscopic assay for the simultaneous detection of oocysts/cysts of Cryptosporidium and G. lamblia in vegetable and water samples. The device consisted of a ball lens 1 mm in diameter, an aluminum mounting plate to transform a smartphone, and a white LED as an illumination source. After concentration of oocysts and staining with Lugol's iodine, oocysts were counted using the smartphone microscope. In comparison to commercial bright field and fluorescence microscopes, the smartphone-based microscopic assay was a low-cost alternative for screening oocysts/cysts of Cryptosporidium spp., and G. lamblia. The LoD of Giardia ranged from 24 cysts/100 g for cucumber to 73 cysts/100 g for cabbage. The LoD for Cryptosporidium ranged from 11 oocysts/100 g for radish to 25 oocysts/100 g for cabbage, while the LoD of Cryptosporidium was lower than that of Giardia (Shrestha et al., 2020) (Figure 12).

Recently, Luka et al. (2021) fabricated a 3D portable and smartphone-integrated on-chip colorimetric biosensor, which was invisible to the naked eye. In this regard, oligonucleotide-modified gold NPs (AuNPs) were used for the detection of Cryptosporidium RNA using UV–Vis spectroscopy. The color change of the AuNPs from red to blue after 5 min was an indicator for Cryptosporidium RNA. The advantages of these methods were the low sample volume (15 μL), short analysis time (~30 min), and high detection limit (5 μM) (Figure 13).

HM and KO: conceived, designed, reviewing, and editing the manuscript. SN: data gathering, literature review, and writing the manuscript. FS: illustrations and graphics. All authors read and approved the final version of the manuscript.